# model
model_name = "RetNet"
use_checkpoint = None
feature_size = 2048
hidden_size = 256
max_frames = 30
nbits = 16
S5VH_type = 'small'

# dataset
dataset = 'activitynet'
workers = 1
batch_size = 128
mask_ratio = 0.5 

# train
seed = 1
num_epochs = 350  
alpha = 0.2
temperature = 0.5
tau_plus = 0.05
train_num_sample = 9722

# Reliability-Aware Hash Center Alignment
a_cluster = 0.001
temperature_cluster = 0.5
nclusters  = 100
warm_epoch = 50
smoothing_alpha = 0.01 

# test
test_batch_size = 128
test_num_sample = 3758
query_num_sample = 1000

# optimizer
optimizer_name = 'AdamW'
schedule ="CosineAnnealingLR" #'StepLR'
lr = 5e-4
min_lr = 1e-5

# path
data_root = '/data2/lianniu/dataset/act/'
home_root = '/data2/lianniu/'

# path:train
train_feat_path = [data_root + 'train_feats.h5']
train_assist_path = data_root+'final_train_train_assit.h5' 
latent_feat_path = data_root+'final_train_latent_feats.h5'
anchor_path = data_root+'final_train_anchors.h5'
sim_path = data_root+'final_train_sim_matrix.h5'

# path:test
test_feat_path = [data_root + 'test_feats.h5'] # database
label_path = [data_root + 're_label.mat']
query_feat_path = [data_root + 'query_feats.h5'] # query
query_label_path = [data_root + 'q_label.mat']

# path:save
save_dir = home_root + 'saved_model/' + dataset + "/" + model_name
file_path = save_dir + '_' + str(nbits) + 'bit'
